Person: Molero, G.
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Molero
First Name
G.
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Molero, G.
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0000-0002-6431-7563 11 results
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- Evidence for increasing global wheat yield potential(IOP Publishing, 2022) Guarin, J.R.; Martre, P.; Ewert, F.; Webber, H.; Dueri, S.; Calderini, D.; Reynolds, M.P.; Molero, G.; Miralles, D.; Garcia, G.; Slafer, G.; Giunta, F.; Pequeno, D.N.L.; Stella, T.; Mukhtar Ahmed; Alderman, P.D.; Basso, B.; Berger, A.G.; Bindi, M.; Bracho-Mujica, G.; Cammarano, D.; Yi Chen; Dumont, B.; Rezaei, E.E.; Fereres, E.; Ferrise, R.; Gaiser, T.; Yujing Gao; Garcia-Vila, M.; Gayler, S.; Hochman, Z.; Hoogenboom, G.; Hunt, L.A.; Kersebaum, K.C.; Nendel, C.; Olesen, J.E.; Palosuo, T.; Priesack, E.; Pullens, J.W.M.; Rodríguez, A.; Rötter, R.P.; Ruiz Ramos, M.; Semenov, M.A.; Senapati, N.; Siebert, S.; Srivastava, A.K.; Stöckle, C.; Supit, I.; Fulu Tao; Thorburn, P.J.; Wang, E.; Weber, T.K.D.; Liujun Xiao; Zhao Zhang; Chuang Zhao; Zhao, J.; Zhigan Zhao; Yan Zhu; Asseng, S.
Publication - Phenotypic variation in photosynthetic traits in wheat grown under field versus glasshouse conditions(Oxford University Press, 2022) Sales, C.R.G.; Molero, G.; Evans, J.R.; Taylor, S.H.; Joynson, R.; Furbank, R.; Hall, A.J.W.; Carmo Silva, E.
Publication - Prediction of photosynthetic, biophysical, and biochemical traits in wheat canopies to reduce the phenotyping bottleneck(Frontiers, 2022) Robles-Zazueta, C.A.; Pinto Espinosa, F.; Molero, G.; Foulkes, J.; Reynolds, M.P.; Murchie, E.
Publication - Relative contribution of shoot and ear photosynthesis to grain filling in wheat under good agronomical conditions assessed by differential organ Delta13C(Oxford University Press, 2014) Sanchez-Bragado, R.; Molero, G.; Reynolds, M.P.; Araus, J.L.
Publication - Estimating organ contribution to grain filling and potential for source upregulation in wheat cultivars with a contrasting source-sink balance(MDPI, 2020) Rivera-Amado, C.; Molero, G.; Trujillo, E.; Reynolds, M.P.; Foulkes, J.
Publication - Estimating organ contribution to grain filling and potential for source upregulation in wheat cultivars with a contrasting source–sink balance(MDPI, 2020) Rivera-Amado, C.; Molero, G.; Trujillo, E.; Reynolds, M.P.; Foulkes, J.
Publication - AP01 - Exploring genetic diversity for biomass and traits related to canopy photosynthesis(CIMMYT, 2019) Molero, G.; Pinto Espinosa, F.; Piñera Chavez, F.J; Rivera-Amado, C.; Sukumaran, S.; Gimeno, J.; Saint Pierre, C.; Reynolds, M.P.
Publication - AP01 - Exploring Genetic Diversity for Biomass and Traits Related to Canopy Photosynthesis(CIMMYT, 2018) Molero, G.; Pinto Espinosa, F.; Piñera Chavez, F.J; Rivera-Amado, C.; Sukumaran, S.; Reynolds, M.P.
Publication - Precision phenotyping: promising physiological traits that can be used in strategic crossing(CIMMYT, 2018) Molero, G.
Publication - Hyperspectral reflectance as a tool to measure biochemical and physiological traits in wheat(Oxford University Press, 2018) Silva-Pérez, V.; Molero, G.; Serbin, P.S.; Condon, A.; Reynolds, M.P.; Furbank, R.; Evans, J.R.Improving photosynthesis to raise wheat yield potential has emerged as a major target for wheat physiologists. Photosynthesis-related traits, such as nitrogen per unit leaf area (Narea) and leaf dry mass per area (LMA), require laborious, destructive, laboratory-based methods, while physiological traits underpinning photosynthetic capacity, such as maximum Rubisco activity normalized to 25 °C (Vcmax25) and electron transport rate (J), require time-consuming gas exchange measurements. The aim of this study was to assess whether hyperspectral reflectance (350–2500 nm) can be used to rapidly estimate these traits on intact wheat leaves. Predictive models were constructed using gas exchange and hyperspectral reflectance data from 76 genotypes grown in glasshouses with different nitrogen levels and/or in the field under yield potential conditions. Models were developed using half of the observed data with the remainder used for validation, yielding correlation coefficients (R2 values) of 0.62 for Vcmax25, 0.7 for J, 0.81 for SPAD, 0.89 for LMA, and 0.93 for Narea, with bias <0.7%. The models were tested on elite lines and landraces that had not been used to create the models. The bias varied between −2.3% and −5.5% while relative error of prediction was similar for SPAD but slightly greater for LMA and Narea.
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